首页|基于贝叶斯自优化Bi-LSTM组合网络的高速铁路轨道-桥梁系统震后响应预测方法

基于贝叶斯自优化Bi-LSTM组合网络的高速铁路轨道-桥梁系统震后响应预测方法

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中国高速铁路(HSR)规划建设逐渐向地震易发地区延伸，亟需一种及时、准确的灾后地震响应快速预测方法，以实现高速铁路系统运输生命线安全的快速评估。本文提出了一种基于贝叶斯自优化双向长短期记忆(Bi-LSTM)网络的快速预测方法，以经过实验验证的高速铁路轨道-桥梁系统有限元模型地震动响应计算数据为样本，将预测地震响应和有限元计算结果进行比较，验证所提方法的精度和鲁棒性，表明该方法在预测高速铁路桥梁结构的非线性地震反应方面是有效的，且高速铁路轨道-桥梁系统的不同预测位置对预测精度的影响不明显;此外，为了降低神经网络训练数据量需求，提出了一种基于离散小波分解的分层聚类算法，结果表明，基于小波分解的分层聚类方法在保证预测精度的同时，有效地减少了训练地震集的输入数量。
A seismic response prediction method based on a self-optimized Bayesian Bi-LSTM mixed network for high-speed railway track-bridge system
The construction of China's high-speed railway(HSR)network has reached earthquake-prone regions,necessitating a timely and accurate post-disaster quick prediction approach to ensure the safety of the HSR systems'transportation lifeline.This study proposes a fast prediction method utilizing a Bayesian self-optimized bi-directional long short-term memory(Bi-LSTM)network to develop a fast prediction framework for the seismic response of the HSR track-bridge system.It describes a hierarchical clustering algorithm based on discrete wavelet decomposition.The results indicated that the proposed framework effectively predicts the nonlinear seismic response of HSR bridge structures.The model also showed the performance of the work migrate ability and robustness.In addition,the impact of different prediction locations on the HSR track-bridge system is minimal.The hierarchical clustering method based on wavelet decomposition can effectively reduce the number of inputs to the seismic training dataset while ensuring prediction accuracy.

high-speed railway track-bridge systemBayesian optimizationBi-LSTM neural networkwavelet clustering

彭康、蒋丽忠、周旺保、余建、向平、吴凌旭

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National Engineering Research Center of High-speed Railway Construction Technology,Central South University,Changsha 410075,China

高速铁路轨道-桥梁系统 贝叶斯优化 Bi-LSTM神经网络 离散小波分解 聚类分析

国家自然科学基金国家自然科学基金国家自然科学基金国家重点研发计划国家重点研发计划Hunan Province Science and Technology Talent Lifting,ChinaScience and Technology Development Fund,ChinaState Key Laboratory of Internet of Things for Smart City(University of Macau),China

52078487U1934207521781802022YFB23026032022YFC30043042022TJ-Y10SKL-IOTSCUM-2021-2023SKL-IOTSCUM-2024-2026/ORP/GA08/2023

2024

10.1007/s11771-024-5571-1

中南大学学报(英文版)
中南大学

中南大学学报(英文版)

CSTPCDEI
影响因子：0.47
ISSN：2095-2899
年,卷(期)：2024.31(3)
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